Companies and societies are at the precipice of rebuilding their foundations to compete in an age of advanced analytics, artificial intelligence (AI), and machine learning (ML). Yet, in the real economy—or in the world outside the tech companies—I see more struggle than success in making advanced analytics and AI a management discipline.

Most leaders in these companies recognize that the perfect storm of big data, computing capacity, and algorithmic advances has arrived. They hear about spectacular use cases such as AI outperforming trained radiologists in detecting retinopathy in preemies. Research also shows that text analytics of earnings calls reveal that executives’ use of euphemisms (think “headwinds”) obscures the details of bad news and delays negative investor reaction. Yet, many leaders feel unsure about this new environment and are struggling to extract value from these cutting-edge technologies.

The outbreak of the Covid-19 virus in China and the railway disruptions across Canada represent two different yet similar classic case studies. They remind us that nations and global economies are becoming increasingly interconnected. Incidents thousands of kilometers away are being felt locally. This is a result of the increasing importance of critical infrastructure (CI).

In order to mitigate these negative consequences to organizations—like lost revenue, lost customers and reputational damage—they must have well-structured and defined contingency plans in place to meet operational objectives.

What’s known as critical infrastructure has many different definitions within academic literature and among different governments worldwide. But essentially, CI can be defined as infrastructure so vital that its incapacity or destruction would have a debilitating impact on the economy or the defense of the country and therefore becomes a national security issue.

A large portion of a digital project manager’s job is making sure the right parts of the project are being worked on. Projects need to be prioritized. Tasks within projects need to be prioritized, too.

Plan View’s Project and Portfolio Management Landscape Report found that prioritization was consistently the second biggest challenge that organizations face. Also, McKinsley surveyed 1,500 professionals and found that only 9 percent were happy with their time allocation.

Many famous writers, businesspeople, and global influencers have stressed the importance of getting your priorities in order.

Mark Twain famously said, “To change your life, you need to change your priorities.”

The same applies to project management.

Each article in this series presents new tools for increasing return on investment (ROI), enhancing customer satisfaction, creating process excellence, and driving risk from an ISO 9001:2015-based quality management system (QMS). They will help implementers evolve quality management to overall business management. In this article we look at demonstrating and establishing various subclauses of Clause 5—Leadership, to build organizational excellence and assess risk.

Clause 5—Leadership

Words have meaning. Throughout the history of ISO 9001, the terms “top management” or “senior management” have been used to describe an organization’s decision and policy makers. These individuals are ostensibly those who are accountable to themselves or to a board of directors for the company’s success. They have the power to hire and fire, and to establish the organization’s operational infrastructure.

In work with more than 700 companies, the term “management” was most often appropriate because those in charge were directors and benevolent dictators. Very few were true leaders of people who created an environment where everyone could achieve their highest level of success and excellence.

When the 2019 Nobel Prize in Economics was announced, it was an exceptional moment because in addition to celebrating contributions to economic science, a methodology was also recognized. The laureates Abhijit Banerjee, Esther Duflo, and Michael Kremer are known for their work in randomized controlled trials (RCTs), which develop evidence-based recommendations for public policy and organizations in the fight against global poverty.

Work with RCTs has led to the identification and implementation of impactful poverty-reduction policies in areas such as skills training and agriculture. But as a methodology, RCTs are not only for the developing world. They have been used for decades in pharmaceutical companies’ clinical trials, and in this age of data, they hold enormous promise to identify business solutions and support evidence-based management.

A recent study showing that data entry is one the most redundant and hated workplace tasks raises questions about why, in the age of artificial intelligence, data mining, and smart technologies, this task is still being done manually.

Is there any way it could be less despised?

My ongoing fieldwork in a data-driven startup, referred to as Sage (a real company, but not its real name due to confidentiality requirements), suggests that technological solutions are not nearly as sophisticated as many assume—and are not going to replace human data entry any time soon.

For nearly two years, I’ve been studying the evolution of Sage’s hiring practices and jobs.